paragraph4
.0em plus.0ex minus.2ex-.5emZero-shot/Open-vocabulary visual recognition and representation learning. Zero-shot and open-vocabulary recognition has been a long-standing problem in computer vision. Earlier works use the visual attributes to represent categories as binary codebooks and learn to predict the attributes for novel categories  [1]}, [2]}. DeViSE [3]} and ConSE [4]} pioneer to learn a joint image-text embedding space using deep learning. Many works have shown the promise of representation learning from natural language associated with images, such as image tags [5]}, [6]}, [7]} or text descriptions [8]}, [9]}, [10]}, [11]}, [12]}. Recently, popular large VLMs scale up by training on billions of image-text pairs and acquire strong image-text representation by contrastive learning [13]}, [14]}, [15]}, [16]}. These models achieve strong zero-shot performance on many classification benchmarks and show clear benefits in scaling model capacity.
